Developments in the last few years have shown that carbon nanotubes (CNTs) can be used in a wide variety of applications due to their size, inert chemical composition, and unique electronic properties. On a microscopic scale, these applications may range from use in targeted cancer cell destruction to potential uses in terahertz imaging. On a macroscopic scale, their incredibly high strength to weight ratio makes them extremely promising in a wide variety of structural applications. In order to use them as a building material, many researchers are looking to produce bulk quantities of long carbon nanotubes. Other researchers are more interested in producing ultra-short carbon nanotubes (US-CNTs), generally less than 50 nm, as these ultra-short tubes allow for the modulation of their electronic properties and are likely to reduce the potentially toxic side effects that their longer counterparts produce.
An extraordinary number of such applications for CNTs require precise methods for controlling the lengths of CNTs. Generally lengths for CNTs can span more than 9 orders of magnitude, and different methods are required for determining CNT lengths within different parts of this length spectrum. Development of such length-control methods requires that CNT samples be characterized in order to evaluate the effectiveness of a given technique. With no single procedure being able to span the entire range of required CNT lengths it is very likely that the need for accurate length distribution data will continue. Even at the lower end of their length spectrum, lengths on the order of nanometers to micrometers, their large aspect ratio makes many types of microscopy difficult when the ultimate goal is to obtain length distribution data. For instance, while transmission electron microscopy (TEM) is able to resolve the internal structure of CNTs better than any other known technique, its small field of view makes it almost useless for extracting length distribution data.
On the other hand, techniques such as scanning electron microscopy (SEM) have a much larger field of view but cannot obtain the same level of resolution necessary to distinguish individual tubes with the accuracy required. Indeed, all currently known forms of optical microscopy are unable to resolve CNTs at all. Again, the issue here is that even millimeter long CNTs have widths that are on the order of nanometers. Currently, scanning probe microscopy (SPM) is the best equipped form of microscopy to extract length distribution data of CNTs. With SPM, and most specifically atomic force microscopy (AFM), a specific form of SPM, individual nanotubes may be resolved in a 10×10 micrometer field of view while larger fields of view may resolve individual CNT ropes and bundles.
Currently, software available for characterization of SPM data focuses on the characterization of gross topological features such as quantifying the overall roughness of a sample or bump analysis. While these gross characterization tools are very useful and necessary, they often do little to characterize individual features or objects visible in the image. The only comparable software currently available is produced by Smart Imaging Technologies, a software package that they call SIMAGIS®. The SIMAGIS software itself is a general image analysis suite for which specific modules may be purchased that extend the software's functionality. However, these modules are often simply an addition to a very large and costly base program that often contains far more features than are needed or desired. Furthermore, these large platforms are often unable to adapt the fundamental inner-workings of their program to best fit the job at hand. Additionally, a larger code base to maintain means that, in general, the SIMAGIS package cannot adapt to fundamentally new requirements as quickly as a smaller piece of software more focused in its purpose. Smart Imaging Technologies was unable to extract any meaningful data from a sample nanotube image, because the samples were not flat enough and that their software was unable to extract the relevant data from the background noise.
Therefore, it would be advantageous to be able to extract length distribution data from background noise for CNTs via SPM and specifically by AFM.